"I imagined it would require technical skill-like some sort of advanced prompt engineering where I'd need to specify exactly how each file interacted with every other file. I thought I'd need to understand the "rules" of combining images with audio, or know the exact syntax for referencing multiple inputs. The reality was much simpler. Multi-modal input just means you can throw different types of files at Seedance 2.0 and tell the model"
"Three high-quality product photographs of their different bean varieties A 5-second video clip of someone pouring coffee into a cup (they'd shot it themselves) A 3-second audio clip of coffee brewing sounds A brief description of the mood they wanted: "warm, inviting, craft-focused" Normally, I would have had to choose between using the images OR the video OR the audio in post-production. I'd create one asset and try to make it work, leaving other materials unused."
Multi-modal input allows combining images, video, audio, and text as inputs to a single video-generation model. Many users expect complex prompt engineering or special syntax to coordinate multiple file types, but multi-modal input simply supplies more information to the model. In one project for a coffee roastery, provided assets included product photographs, a short pouring clip, a brewing audio clip, and a mood description of "warm, inviting, craft-focused". Traditional post-production often forces a choice among assets, while multi-modal capability enables using all materials simultaneously to produce a cohesive promotional video.
Read at Business Matters
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